Overview

Dataset statistics

Number of variables28
Number of observations1780807
Missing cells125407
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory275.1 MiB
Average record size in memory162.0 B

Variable types

Categorical10
Numeric16
Text1
DateTime1

Alerts

addr_state has a high cardinality: 51 distinct valuesHigh cardinality
application_type is highly imbalanced (76.5%)Imbalance
emp_title has 125407 (7.0%) missing valuesMissing
annual_inc is highly skewed (γ1 = 516.8210002)Skewed
dti is highly skewed (γ1 = 29.73491815)Skewed
bc_util has 23343 (1.3%) zerosZeros
chargeoff_within_12_mths has 1766309 (99.2%) zerosZeros
delinq_2yrs has 1438977 (80.8%) zerosZeros
inq_last_6mths has 1058418 (59.4%) zerosZeros
last_fico_range_low has 47732 (2.7%) zerosZeros

Reproduction

Analysis started2024-07-08 04:33:21.373994
Analysis finished2024-07-08 04:33:56.067517
Duration34.69 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

addr_state
Categorical

HIGH CARDINALITY 

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
CA
250934 
TX
147053 
NY
144022 
FL
128052 
IL
 
69826
Other values (46)
1040920 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3561614
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNC
2nd rowTX
3rd rowMI
4th rowTX
5th rowNC

Common Values

ValueCountFrequency (%)
CA 250934
 
14.1%
TX 147053
 
8.3%
NY 144022
 
8.1%
FL 128052
 
7.2%
IL 69826
 
3.9%
NJ 63987
 
3.6%
PA 59851
 
3.4%
OH 58668
 
3.3%
GA 57985
 
3.3%
NC 49925
 
2.8%
Other values (41) 750504
42.1%

Length

2024-07-07T23:33:56.094856image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca 250934
 
14.1%
tx 147053
 
8.3%
ny 144022
 
8.1%
fl 128052
 
7.2%
il 69826
 
3.9%
nj 63987
 
3.6%
pa 59851
 
3.4%
oh 58668
 
3.3%
ga 57985
 
3.3%
nc 49925
 
2.8%
Other values (41) 750504
42.1%

Most occurring characters

ValueCountFrequency (%)
A 599499
16.8%
N 402601
11.3%
C 392331
11.0%
L 239692
 
6.7%
T 224180
 
6.3%
M 217183
 
6.1%
I 190625
 
5.4%
Y 165008
 
4.6%
O 163751
 
4.6%
X 147053
 
4.1%
Other values (14) 819691
23.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 599499
16.8%
N 402601
11.3%
C 392331
11.0%
L 239692
 
6.7%
T 224180
 
6.3%
M 217183
 
6.1%
I 190625
 
5.4%
Y 165008
 
4.6%
O 163751
 
4.6%
X 147053
 
4.1%
Other values (14) 819691
23.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 599499
16.8%
N 402601
11.3%
C 392331
11.0%
L 239692
 
6.7%
T 224180
 
6.3%
M 217183
 
6.1%
I 190625
 
5.4%
Y 165008
 
4.6%
O 163751
 
4.6%
X 147053
 
4.1%
Other values (14) 819691
23.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 599499
16.8%
N 402601
11.3%
C 392331
11.0%
L 239692
 
6.7%
T 224180
 
6.3%
M 217183
 
6.1%
I 190625
 
5.4%
Y 165008
 
4.6%
O 163751
 
4.6%
X 147053
 
4.1%
Other values (14) 819691
23.0%

annual_inc
Real number (ℝ)

SKEWED 

Distinct76561
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77885.389
Minimum0
Maximum1.1 × 108
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:56.137250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28000
Q146500
median65000
Q393000
95-th percentile160000
Maximum1.1 × 108
Range1.1 × 108
Interquartile range (IQR)46500

Descriptive statistics

Standard deviation119925.06
Coefficient of variation (CV)1.5397632
Kurtosis434392.92
Mean77885.389
Median Absolute Deviation (MAD)21676
Skewness516.821
Sum1.3869885 × 1011
Variance1.4382019 × 1010
MonotonicityNot monotonic
2024-07-07T23:33:56.185861image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 68747
 
3.9%
50000 60531
 
3.4%
65000 51887
 
2.9%
70000 49417
 
2.8%
80000 47332
 
2.7%
40000 46945
 
2.6%
75000 46307
 
2.6%
45000 43411
 
2.4%
55000 41296
 
2.3%
100000 36997
 
2.1%
Other values (76551) 1287937
72.3%
ValueCountFrequency (%)
0 4
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
20 2
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
32 1
 
< 0.1%
33 1
 
< 0.1%
39 1
 
< 0.1%
40 1
 
< 0.1%
ValueCountFrequency (%)
110000000 1
< 0.1%
61000000 1
< 0.1%
10999200 1
< 0.1%
9573072 1
< 0.1%
9550000 1
< 0.1%
9522972 1
< 0.1%
9500000 1
< 0.1%
9300000 1
< 0.1%
9225000 1
< 0.1%
9100000 1
< 0.1%

application_type
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Individual
1712446 
Joint App
 
68361

Length

Max length10
Median length10
Mean length9.9616123
Min length9

Characters and Unicode

Total characters17739709
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 1712446
96.2%
Joint App 68361
 
3.8%

Length

2024-07-07T23:33:56.228654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:56.264654image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
individual 1712446
92.6%
joint 68361
 
3.7%
app 68361
 
3.7%

Most occurring characters

ValueCountFrequency (%)
i 3493253
19.7%
d 3424892
19.3%
n 1780807
10.0%
I 1712446
9.7%
v 1712446
9.7%
u 1712446
9.7%
a 1712446
9.7%
l 1712446
9.7%
p 136722
 
0.8%
J 68361
 
0.4%
Other values (4) 273444
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17739709
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 3493253
19.7%
d 3424892
19.3%
n 1780807
10.0%
I 1712446
9.7%
v 1712446
9.7%
u 1712446
9.7%
a 1712446
9.7%
l 1712446
9.7%
p 136722
 
0.8%
J 68361
 
0.4%
Other values (4) 273444
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17739709
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 3493253
19.7%
d 3424892
19.3%
n 1780807
10.0%
I 1712446
9.7%
v 1712446
9.7%
u 1712446
9.7%
a 1712446
9.7%
l 1712446
9.7%
p 136722
 
0.8%
J 68361
 
0.4%
Other values (4) 273444
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17739709
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 3493253
19.7%
d 3424892
19.3%
n 1780807
10.0%
I 1712446
9.7%
v 1712446
9.7%
u 1712446
9.7%
a 1712446
9.7%
l 1712446
9.7%
p 136722
 
0.8%
J 68361
 
0.4%
Other values (4) 273444
 
1.5%

avg_cur_bal
Real number (ℝ)

Distinct83440
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13478.885
Minimum0
Maximum555925
Zeros614
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:56.301626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1080
Q13083
median7367
Q318738
95-th percentile43186
Maximum555925
Range555925
Interquartile range (IQR)15655

Descriptive statistics

Standard deviation16169.577
Coefficient of variation (CV)1.1996227
Kurtosis28.74356
Mean13478.885
Median Absolute Deviation (MAD)5392
Skewness3.4862601
Sum2.4003294 × 1010
Variance2.6145523 × 108
MonotonicityNot monotonic
2024-07-07T23:33:56.347065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 614
 
< 0.1%
1971 229
 
< 0.1%
2253 228
 
< 0.1%
2149 224
 
< 0.1%
2842 221
 
< 0.1%
2079 221
 
< 0.1%
2606 221
 
< 0.1%
2076 221
 
< 0.1%
2447 220
 
< 0.1%
2301 220
 
< 0.1%
Other values (83430) 1778188
99.9%
ValueCountFrequency (%)
0 614
< 0.1%
1 53
 
< 0.1%
2 46
 
< 0.1%
3 50
 
< 0.1%
4 26
 
< 0.1%
5 42
 
< 0.1%
6 38
 
< 0.1%
7 29
 
< 0.1%
8 30
 
< 0.1%
9 33
 
< 0.1%
ValueCountFrequency (%)
555925 1
< 0.1%
502002 1
< 0.1%
498284 1
< 0.1%
497484 1
< 0.1%
478909 1
< 0.1%
477255 1
< 0.1%
466840 1
< 0.1%
463945 1
< 0.1%
463276 1
< 0.1%
447102 1
< 0.1%

bc_util
Real number (ℝ)

ZEROS 

Distinct1476
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.080035
Minimum0
Maximum339.6
Zeros23343
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:56.392402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.4
Q135.5
median60.6
Q383.4
95-th percentile97.9
Maximum339.6
Range339.6
Interquartile range (IQR)47.9

Descriptive statistics

Standard deviation28.712832
Coefficient of variation (CV)0.49436665
Kurtosis-0.99516944
Mean58.080035
Median Absolute Deviation (MAD)23.8
Skewness-0.28259824
Sum1.0342933 × 108
Variance824.42673
MonotonicityNot monotonic
2024-07-07T23:33:56.440078image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23343
 
1.3%
98 4957
 
0.3%
97 4666
 
0.3%
99 4596
 
0.3%
96 4517
 
0.3%
95 4194
 
0.2%
94 3983
 
0.2%
93 3800
 
0.2%
92 3671
 
0.2%
91 3551
 
0.2%
Other values (1466) 1719529
96.6%
ValueCountFrequency (%)
0 23343
1.3%
0.1 1949
 
0.1%
0.2 1705
 
0.1%
0.3 1445
 
0.1%
0.4 1244
 
0.1%
0.5 1187
 
0.1%
0.6 1086
 
0.1%
0.7 1076
 
0.1%
0.8 1029
 
0.1%
0.9 970
 
0.1%
ValueCountFrequency (%)
339.6 1
< 0.1%
318.2 1
< 0.1%
255.2 1
< 0.1%
252.3 1
< 0.1%
243.8 1
< 0.1%
235.3 1
< 0.1%
204.6 1
< 0.1%
202.9 1
< 0.1%
202 1
< 0.1%
201.9 1
< 0.1%

chargeoff_within_12_mths
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0091183379
Minimum0
Maximum10
Zeros1766309
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:56.480920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.10969887
Coefficient of variation (CV)12.030577
Kurtosis604.95495
Mean0.0091183379
Median Absolute Deviation (MAD)0
Skewness18.073747
Sum16238
Variance0.012033841
MonotonicityNot monotonic
2024-07-07T23:33:56.515741image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 1766309
99.2%
1 13249
 
0.7%
2 974
 
0.1%
3 165
 
< 0.1%
4 63
 
< 0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
7 6
 
< 0.1%
9 6
 
< 0.1%
8 2
 
< 0.1%
ValueCountFrequency (%)
0 1766309
99.2%
1 13249
 
0.7%
2 974
 
0.1%
3 165
 
< 0.1%
4 63
 
< 0.1%
5 20
 
< 0.1%
6 12
 
< 0.1%
7 6
 
< 0.1%
8 2
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
9 6
 
< 0.1%
8 2
 
< 0.1%
7 6
 
< 0.1%
6 12
 
< 0.1%
5 20
 
< 0.1%
4 63
 
< 0.1%
3 165
 
< 0.1%
2 974
 
0.1%
1 13249
0.7%

delinq_2yrs
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31743024
Minimum0
Maximum39
Zeros1438977
Zeros (%)80.8%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:56.553826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum39
Range39
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.88197411
Coefficient of variation (CV)2.7784817
Kurtosis60.025758
Mean0.31743024
Median Absolute Deviation (MAD)0
Skewness5.6693133
Sum565282
Variance0.77787833
MonotonicityNot monotonic
2024-07-07T23:33:56.598630image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 1438977
80.8%
1 227186
 
12.8%
2 66195
 
3.7%
3 24223
 
1.4%
4 10833
 
0.6%
5 5451
 
0.3%
6 3102
 
0.2%
7 1712
 
0.1%
8 1044
 
0.1%
9 653
 
< 0.1%
Other values (24) 1431
 
0.1%
ValueCountFrequency (%)
0 1438977
80.8%
1 227186
 
12.8%
2 66195
 
3.7%
3 24223
 
1.4%
4 10833
 
0.6%
5 5451
 
0.3%
6 3102
 
0.2%
7 1712
 
0.1%
8 1044
 
0.1%
9 653
 
< 0.1%
ValueCountFrequency (%)
39 1
 
< 0.1%
36 1
 
< 0.1%
32 1
 
< 0.1%
30 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
26 3
< 0.1%
25 2
< 0.1%
24 1
 
< 0.1%

dti
Real number (ℝ)

SKEWED 

Distinct9349
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.746592
Minimum-1
Maximum999
Zeros971
Zeros (%)0.1%
Negative2
Negative (%)< 0.1%
Memory size27.2 MiB
2024-07-07T23:33:56.645656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile5.07
Q111.96
median17.88
Q324.52
95-th percentile33.6
Maximum999
Range1000
Interquartile range (IQR)12.56

Descriptive statistics

Standard deviation13.228308
Coefficient of variation (CV)0.70563803
Kurtosis1935.2832
Mean18.746592
Median Absolute Deviation (MAD)6.24
Skewness29.734918
Sum33384062
Variance174.98813
MonotonicityNot monotonic
2024-07-07T23:33:56.692374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.2 1271
 
0.1%
14.4 1254
 
0.1%
18 1250
 
0.1%
16.8 1244
 
0.1%
13.2 1192
 
0.1%
15.6 1187
 
0.1%
20.4 1152
 
0.1%
12 1143
 
0.1%
21.6 1117
 
0.1%
10.8 1059
 
0.1%
Other values (9339) 1768938
99.3%
ValueCountFrequency (%)
-1 2
 
< 0.1%
0 971
0.1%
0.01 12
 
< 0.1%
0.02 24
 
< 0.1%
0.03 14
 
< 0.1%
0.04 9
 
< 0.1%
0.05 15
 
< 0.1%
0.06 27
 
< 0.1%
0.07 20
 
< 0.1%
0.08 22
 
< 0.1%
ValueCountFrequency (%)
999 89
< 0.1%
994.4 1
 
< 0.1%
991.57 1
 
< 0.1%
962.83 1
 
< 0.1%
962.12 1
 
< 0.1%
942.17 1
 
< 0.1%
917.87 1
 
< 0.1%
893.1 1
 
< 0.1%
886.77 1
 
< 0.1%
879.55 1
 
< 0.1%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
10
590673 
0
259066 
2
160692 
3
142512 
1
117185 
Other values (6)
510679 

Length

Max length2
Median length1
Mean length1.3316884
Min length1

Characters and Unicode

Total characters2371480
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row2
3rd row10
4th row3
5th row4

Common Values

ValueCountFrequency (%)
10 590673
33.2%
0 259066
14.5%
2 160692
 
9.0%
3 142512
 
8.0%
1 117185
 
6.6%
5 109508
 
6.1%
4 106030
 
6.0%
6 80737
 
4.5%
8 75519
 
4.2%
7 74368
 
4.2%

Length

2024-07-07T23:33:56.734433image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10 590673
33.2%
0 259066
14.5%
2 160692
 
9.0%
3 142512
 
8.0%
1 117185
 
6.6%
5 109508
 
6.1%
4 106030
 
6.0%
6 80737
 
4.5%
8 75519
 
4.2%
7 74368
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 849739
35.8%
1 707858
29.8%
2 160692
 
6.8%
3 142512
 
6.0%
5 109508
 
4.6%
4 106030
 
4.5%
6 80737
 
3.4%
8 75519
 
3.2%
7 74368
 
3.1%
9 64517
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2371480
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 849739
35.8%
1 707858
29.8%
2 160692
 
6.8%
3 142512
 
6.0%
5 109508
 
4.6%
4 106030
 
4.5%
6 80737
 
3.4%
8 75519
 
3.2%
7 74368
 
3.1%
9 64517
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2371480
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 849739
35.8%
1 707858
29.8%
2 160692
 
6.8%
3 142512
 
6.0%
5 109508
 
4.6%
4 106030
 
4.5%
6 80737
 
3.4%
8 75519
 
3.2%
7 74368
 
3.1%
9 64517
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2371480
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 849739
35.8%
1 707858
29.8%
2 160692
 
6.8%
3 142512
 
6.0%
5 109508
 
4.6%
4 106030
 
4.5%
6 80737
 
3.4%
8 75519
 
3.2%
7 74368
 
3.1%
9 64517
 
2.7%

emp_title
Text

MISSING 

Distinct355491
Distinct (%)21.5%
Missing125407
Missing (%)7.0%
Memory size27.2 MiB
2024-07-07T23:33:56.891259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length42
Median length32
Mean length15.573088
Min length1

Characters and Unicode

Total characters25779690
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique269759 ?
Unique (%)16.3%

Sample

1st rowPROJECT_MANAGER
2nd rowSURGICAL_TECHNICIAN
3rd rowTEAM_LEADERN_CUSTOMER_OPS_&_SYSTEMS
4th rowSYSTEMS_ENGINEER
5th rowASSISTANT_DIRECTOR_-_HUMAN_RESOURCES
ValueCountFrequency (%)
teacher 38067
 
2.3%
manager 37323
 
2.3%
owner 24645
 
1.5%
registered_nurse 18555
 
1.1%
supervisor 18030
 
1.1%
driver 17899
 
1.1%
sales 15581
 
0.9%
rn 14024
 
0.8%
office_manager 11308
 
0.7%
project_manager 11219
 
0.7%
Other values (332086) 1448762
87.5%
2024-07-07T23:33:57.118112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2969283
11.5%
R 2430919
9.4%
A 2377113
 
9.2%
I 1937324
 
7.5%
N 1865993
 
7.2%
_ 1860173
 
7.2%
T 1822576
 
7.1%
S 1713995
 
6.6%
O 1446120
 
5.6%
C 1395271
 
5.4%
Other values (121) 5960923
23.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25779690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 2969283
11.5%
R 2430919
9.4%
A 2377113
 
9.2%
I 1937324
 
7.5%
N 1865993
 
7.2%
_ 1860173
 
7.2%
T 1822576
 
7.1%
S 1713995
 
6.6%
O 1446120
 
5.6%
C 1395271
 
5.4%
Other values (121) 5960923
23.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25779690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 2969283
11.5%
R 2430919
9.4%
A 2377113
 
9.2%
I 1937324
 
7.5%
N 1865993
 
7.2%
_ 1860173
 
7.2%
T 1822576
 
7.1%
S 1713995
 
6.6%
O 1446120
 
5.6%
C 1395271
 
5.4%
Other values (121) 5960923
23.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25779690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 2969283
11.5%
R 2430919
9.4%
A 2377113
 
9.2%
I 1937324
 
7.5%
N 1865993
 
7.2%
_ 1860173
 
7.2%
T 1822576
 
7.1%
S 1713995
 
6.6%
O 1446120
 
5.6%
C 1395271
 
5.4%
Other values (121) 5960923
23.1%

fico_range_high
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean701.17756
Minimum664
Maximum850
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:57.178775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum664
5-th percentile664
Q1674
median694
Q3719
95-th percentile769
Maximum850
Range186
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.487435
Coefficient of variation (CV)0.04633268
Kurtosis1.5508258
Mean701.17756
Median Absolute Deviation (MAD)20
Skewness1.2599387
Sum1.2486619 × 109
Variance1055.4335
MonotonicityNot monotonic
2024-07-07T23:33:57.222649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
664 155764
 
8.7%
674 150673
 
8.5%
669 150202
 
8.4%
684 134656
 
7.6%
679 134595
 
7.6%
689 118377
 
6.6%
694 115285
 
6.5%
699 103553
 
5.8%
704 96756
 
5.4%
709 87721
 
4.9%
Other values (28) 533225
29.9%
ValueCountFrequency (%)
664 155764
8.7%
669 150202
8.4%
674 150673
8.5%
679 134595
7.6%
684 134656
7.6%
689 118377
6.6%
694 115285
6.5%
699 103553
5.8%
704 96756
5.4%
709 87721
4.9%
ValueCountFrequency (%)
850 302
 
< 0.1%
844 398
 
< 0.1%
839 625
 
< 0.1%
834 1080
 
0.1%
829 1612
 
0.1%
824 2139
 
0.1%
819 2923
0.2%
814 3466
0.2%
809 4850
0.3%
804 5710
0.3%

fico_range_low
Real number (ℝ)

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean697.17739
Minimum660
Maximum845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:57.267964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum660
5-th percentile660
Q1670
median690
Q3715
95-th percentile765
Maximum845
Range185
Interquartile range (IQR)45

Descriptive statistics

Standard deviation32.486661
Coefficient of variation (CV)0.046597411
Kurtosis1.5492987
Mean697.17739
Median Absolute Deviation (MAD)20
Skewness1.259718
Sum1.2415384 × 109
Variance1055.3832
MonotonicityNot monotonic
2024-07-07T23:33:57.312213image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
660 155764
 
8.7%
670 150673
 
8.5%
665 150202
 
8.4%
680 134656
 
7.6%
675 134595
 
7.6%
685 118377
 
6.6%
690 115285
 
6.5%
695 103553
 
5.8%
700 96756
 
5.4%
705 87721
 
4.9%
Other values (28) 533225
29.9%
ValueCountFrequency (%)
660 155764
8.7%
665 150202
8.4%
670 150673
8.5%
675 134595
7.6%
680 134656
7.6%
685 118377
6.6%
690 115285
6.5%
695 103553
5.8%
700 96756
5.4%
705 87721
4.9%
ValueCountFrequency (%)
845 302
 
< 0.1%
840 398
 
< 0.1%
835 625
 
< 0.1%
830 1080
 
0.1%
825 1612
 
0.1%
820 2139
 
0.1%
815 2923
0.2%
810 3466
0.2%
805 4850
0.3%
800 5710
0.3%

grade
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
B
522184 
C
510790 
A
328659 
D
263837 
E
110477 
Other values (2)
 
44860

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1780807
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowA
5th rowB

Common Values

ValueCountFrequency (%)
B 522184
29.3%
C 510790
28.7%
A 328659
18.5%
D 263837
14.8%
E 110477
 
6.2%
F 34830
 
2.0%
G 10030
 
0.6%

Length

2024-07-07T23:33:57.354120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:57.391644image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
b 522184
29.3%
c 510790
28.7%
a 328659
18.5%
d 263837
14.8%
e 110477
 
6.2%
f 34830
 
2.0%
g 10030
 
0.6%

Most occurring characters

ValueCountFrequency (%)
B 522184
29.3%
C 510790
28.7%
A 328659
18.5%
D 263837
14.8%
E 110477
 
6.2%
F 34830
 
2.0%
G 10030
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1780807
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 522184
29.3%
C 510790
28.7%
A 328659
18.5%
D 263837
14.8%
E 110477
 
6.2%
F 34830
 
2.0%
G 10030
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1780807
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 522184
29.3%
C 510790
28.7%
A 328659
18.5%
D 263837
14.8%
E 110477
 
6.2%
F 34830
 
2.0%
G 10030
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1780807
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 522184
29.3%
C 510790
28.7%
A 328659
18.5%
D 263837
14.8%
E 110477
 
6.2%
F 34830
 
2.0%
G 10030
 
0.6%

home_ownership
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
MORTGAGE
881467 
RENT
699117 
OWN
198953 
ANY
 
1181
NONE
 
45

Length

Max length8
Median length5
Mean length5.8675679
Min length3

Characters and Unicode

Total characters10449006
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowMORTGAGE
3rd rowOWN
4th rowMORTGAGE
5th rowRENT

Common Values

ValueCountFrequency (%)
MORTGAGE 881467
49.5%
RENT 699117
39.3%
OWN 198953
 
11.2%
ANY 1181
 
0.1%
NONE 45
 
< 0.1%
OTHER 44
 
< 0.1%

Length

2024-07-07T23:33:57.434207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:57.472138image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
mortgage 881467
49.5%
rent 699117
39.3%
own 198953
 
11.2%
any 1181
 
0.1%
none 45
 
< 0.1%
other 44
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 1762934
16.9%
E 1580673
15.1%
R 1580628
15.1%
T 1580628
15.1%
O 1080509
10.3%
N 899341
8.6%
A 882648
8.4%
M 881467
8.4%
W 198953
 
1.9%
Y 1181
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10449006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 1762934
16.9%
E 1580673
15.1%
R 1580628
15.1%
T 1580628
15.1%
O 1080509
10.3%
N 899341
8.6%
A 882648
8.4%
M 881467
8.4%
W 198953
 
1.9%
Y 1181
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10449006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 1762934
16.9%
E 1580673
15.1%
R 1580628
15.1%
T 1580628
15.1%
O 1080509
10.3%
N 899341
8.6%
A 882648
8.4%
M 881467
8.4%
W 198953
 
1.9%
Y 1181
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10449006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 1762934
16.9%
E 1580673
15.1%
R 1580628
15.1%
T 1580628
15.1%
O 1080509
10.3%
N 899341
8.6%
A 882648
8.4%
M 881467
8.4%
W 198953
 
1.9%
Y 1181
 
< 0.1%

inq_last_6mths
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60525256
Minimum0
Maximum8
Zeros1058418
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:57.509079image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89573932
Coefficient of variation (CV)1.479943
Kurtosis3.5699568
Mean0.60525256
Median Absolute Deviation (MAD)0
Skewness1.7598544
Sum1077838
Variance0.80234893
MonotonicityNot monotonic
2024-07-07T23:33:57.545468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1058418
59.4%
1 476033
26.7%
2 166357
 
9.3%
3 58072
 
3.3%
4 15628
 
0.9%
5 5436
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 1058418
59.4%
1 476033
26.7%
2 166357
 
9.3%
3 58072
 
3.3%
4 15628
 
0.9%
5 5436
 
0.3%
6 859
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 859
 
< 0.1%
5 5436
 
0.3%
4 15628
 
0.9%
3 58072
 
3.3%
2 166357
 
9.3%
1 476033
26.7%
0 1058418
59.4%

installment
Real number (ℝ)

Distinct88561
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean444.65469
Minimum4.93
Maximum1719.83
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:57.587905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4.93
5-th percentile109.75
Q1250.29
median377.04
Q3591.11
95-th percentile988.42
Maximum1719.83
Range1714.9
Interquartile range (IQR)340.82

Descriptive statistics

Standard deviation267.81647
Coefficient of variation (CV)0.60230214
Kurtosis0.70629311
Mean444.65469
Median Absolute Deviation (MAD)158.01
Skewness1.0098912
Sum7.9184418 × 108
Variance71725.661
MonotonicityNot monotonic
2024-07-07T23:33:57.635682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301.15 3860
 
0.2%
332.1 3375
 
0.2%
327.34 3280
 
0.2%
361.38 3151
 
0.2%
602.3 2739
 
0.2%
451.73 2736
 
0.2%
329.72 2560
 
0.1%
318.79 2281
 
0.1%
312.86 2181
 
0.1%
392.81 2168
 
0.1%
Other values (88551) 1752476
98.4%
ValueCountFrequency (%)
4.93 1
< 0.1%
7.61 1
< 0.1%
14.01 1
< 0.1%
14.77 1
< 0.1%
19.4 1
< 0.1%
20.11 1
< 0.1%
23.26 1
< 0.1%
23.36 1
< 0.1%
25.81 1
< 0.1%
25.86 1
< 0.1%
ValueCountFrequency (%)
1719.83 2
 
< 0.1%
1717.63 1
 
< 0.1%
1715.42 2
 
< 0.1%
1714.54 5
< 0.1%
1691.28 2
 
< 0.1%
1676.23 2
 
< 0.1%
1671.88 2
 
< 0.1%
1670.15 1
 
< 0.1%
1664.57 1
 
< 0.1%
1647.03 1
 
< 0.1%

int_rate
Real number (ℝ)

Distinct395
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13186416
Minimum0.0531
Maximum0.3099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:57.685445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0531
5-th percentile0.0649
Q10.0949
median0.1269
Q30.1599
95-th percentile0.2239
Maximum0.3099
Range0.2568
Interquartile range (IQR)0.065

Descriptive statistics

Standard deviation0.04851914
Coefficient of variation (CV)0.3679479
Kurtosis0.55136545
Mean0.13186416
Median Absolute Deviation (MAD)0.0325
Skewness0.75768475
Sum234824.62
Variance0.002354107
MonotonicityNot monotonic
2024-07-07T23:33:57.907334image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1199 41915
 
2.4%
0.0532 41635
 
2.3%
0.1099 41298
 
2.3%
0.1399 36830
 
2.1%
0.1149 29448
 
1.7%
0.1699 28174
 
1.6%
0.1299 27678
 
1.6%
0.0789 27379
 
1.5%
0.0917 26922
 
1.5%
0.1561 24259
 
1.4%
Other values (385) 1455269
81.7%
ValueCountFrequency (%)
0.0531 3294
 
0.2%
0.0532 41635
2.3%
0.0593 1809
 
0.1%
0.06 600
 
< 0.1%
0.0603 9250
 
0.5%
0.0607 2223
 
0.1%
0.0608 2937
 
0.2%
0.0611 3834
 
0.2%
0.0619 1314
 
0.1%
0.0624 7456
 
0.4%
ValueCountFrequency (%)
0.3099 663
< 0.1%
0.3094 571
< 0.1%
0.3089 540
< 0.1%
0.3084 611
< 0.1%
0.3079 1159
0.1%
0.3075 750
< 0.1%
0.3074 392
 
< 0.1%
0.3065 687
< 0.1%
0.3049 440
 
< 0.1%
0.3017 843
< 0.1%
Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size27.2 MiB
Minimum2012-08-01 00:00:00
Maximum2020-09-01 00:00:00
2024-07-07T23:33:57.955552image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:58.006413image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

last_fico_range_high
Real number (ℝ)

Distinct72
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.57609
Minimum0
Maximum850
Zeros178
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:58.053339image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519
Q1624
median694
Q3739
95-th percentile799
Maximum850
Range850
Interquartile range (IQR)115

Descriptive statistics

Standard deviation82.460517
Coefficient of variation (CV)0.12151993
Kurtosis-0.10653194
Mean678.57609
Median Absolute Deviation (MAD)50
Skewness-0.55279513
Sum1.208413 × 109
Variance6799.7369
MonotonicityNot monotonic
2024-07-07T23:33:58.097839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
709 54374
 
3.1%
694 53856
 
3.0%
714 53632
 
3.0%
699 53504
 
3.0%
704 53436
 
3.0%
719 53034
 
3.0%
724 51560
 
2.9%
684 49031
 
2.8%
689 48530
 
2.7%
499 47554
 
2.7%
Other values (62) 1262296
70.9%
ValueCountFrequency (%)
0 178
 
< 0.1%
499 47554
2.7%
504 10136
 
0.6%
509 10827
 
0.6%
514 12227
 
0.7%
519 12526
 
0.7%
524 14127
 
0.8%
529 13923
 
0.8%
534 15741
 
0.9%
539 15800
 
0.9%
ValueCountFrequency (%)
850 364
 
< 0.1%
844 869
 
< 0.1%
839 1644
 
0.1%
834 3251
 
0.2%
829 5387
 
0.3%
824 6942
0.4%
819 10090
0.6%
814 11704
0.7%
809 14522
0.8%
804 17142
1.0%

last_fico_range_low
Real number (ℝ)

ZEROS 

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean661.35799
Minimum0
Maximum845
Zeros47732
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:58.142306image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile515
Q1620
median690
Q3735
95-th percentile795
Maximum845
Range845
Interquartile range (IQR)115

Descriptive statistics

Standard deviation133.84636
Coefficient of variation (CV)0.20238111
Kurtosis13.241018
Mean661.35799
Median Absolute Deviation (MAD)50
Skewness-3.1858893
Sum1.1777509 × 109
Variance17914.848
MonotonicityNot monotonic
2024-07-07T23:33:58.186004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
705 54374
 
3.1%
690 53856
 
3.0%
710 53632
 
3.0%
695 53504
 
3.0%
700 53436
 
3.0%
715 53034
 
3.0%
720 51560
 
2.9%
680 49031
 
2.8%
685 48530
 
2.7%
0 47732
 
2.7%
Other values (61) 1262118
70.9%
ValueCountFrequency (%)
0 47732
2.7%
500 10136
 
0.6%
505 10827
 
0.6%
510 12227
 
0.7%
515 12526
 
0.7%
520 14127
 
0.8%
525 13923
 
0.8%
530 15741
 
0.9%
535 15800
 
0.9%
540 17649
 
1.0%
ValueCountFrequency (%)
845 364
 
< 0.1%
840 869
 
< 0.1%
835 1644
 
0.1%
830 3251
 
0.2%
825 5387
 
0.3%
820 6942
0.4%
815 10090
0.6%
810 11704
0.7%
805 14522
0.8%
800 17142
1.0%

loan_amnt
Real number (ℝ)

Distinct1561
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14736.404
Minimum1000
Maximum40000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:58.230268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile3200
Q18000
median12100
Q320000
95-th percentile35000
Maximum40000
Range39000
Interquartile range (IQR)12000

Descriptive statistics

Standard deviation9000.6551
Coefficient of variation (CV)0.61077689
Kurtosis-0.08851783
Mean14736.404
Median Absolute Deviation (MAD)5900
Skewness0.78861326
Sum2.6242691 × 1010
Variance81011792
MonotonicityNot monotonic
2024-07-07T23:33:58.279863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 139718
 
7.8%
20000 97500
 
5.5%
15000 94562
 
5.3%
12000 94452
 
5.3%
35000 67362
 
3.8%
5000 66407
 
3.7%
8000 61776
 
3.5%
6000 58295
 
3.3%
16000 50325
 
2.8%
25000 48111
 
2.7%
Other values (1551) 1002299
56.3%
ValueCountFrequency (%)
1000 7850
0.4%
1025 30
 
< 0.1%
1050 45
 
< 0.1%
1075 22
 
< 0.1%
1100 225
 
< 0.1%
1125 38
 
< 0.1%
1150 33
 
< 0.1%
1175 15
 
< 0.1%
1200 3099
 
0.2%
1225 16
 
< 0.1%
ValueCountFrequency (%)
40000 18659
1.0%
39975 10
 
< 0.1%
39950 5
 
< 0.1%
39925 6
 
< 0.1%
39900 15
 
< 0.1%
39875 5
 
< 0.1%
39850 4
 
< 0.1%
39825 10
 
< 0.1%
39800 9
 
< 0.1%
39775 9
 
< 0.1%

loan_status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Fully Paid
1422806 
Non-Performing
358001 

Length

Max length14
Median length10
Mean length10.804132
Min length10

Characters and Unicode

Total characters19240074
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowFully Paid
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid

Common Values

ValueCountFrequency (%)
Fully Paid 1422806
79.9%
Non-Performing 358001
 
20.1%

Length

2024-07-07T23:33:58.328317image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:58.365278image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
fully 1422806
44.4%
paid 1422806
44.4%
non-performing 358001
 
11.2%

Most occurring characters

ValueCountFrequency (%)
l 2845612
14.8%
P 1780807
9.3%
i 1780807
9.3%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 716002
 
3.7%
Other values (8) 3580010
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19240074
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2845612
14.8%
P 1780807
9.3%
i 1780807
9.3%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 716002
 
3.7%
Other values (8) 3580010
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19240074
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2845612
14.8%
P 1780807
9.3%
i 1780807
9.3%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 716002
 
3.7%
Other values (8) 3580010
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19240074
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2845612
14.8%
P 1780807
9.3%
i 1780807
9.3%
F 1422806
 
7.4%
y 1422806
 
7.4%
1422806
 
7.4%
a 1422806
 
7.4%
d 1422806
 
7.4%
u 1422806
 
7.4%
r 716002
 
3.7%
Other values (8) 3580010
18.6%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
debt_consolidation
1023751 
credit_card
402392 
home_improvement
117589 
other
105466 
major_purchase
 
38262
Other values (9)
 
93347

Length

Max length18
Median length18
Mean length14.876841
Min length3

Characters and Unicode

Total characters26492782
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebt_consolidation
2nd rowhome_improvement
3rd rowdebt_consolidation
4th rowdebt_consolidation
5th rowdebt_consolidation

Common Values

ValueCountFrequency (%)
debt_consolidation 1023751
57.5%
credit_card 402392
 
22.6%
home_improvement 117589
 
6.6%
other 105466
 
5.9%
major_purchase 38262
 
2.1%
medical 21100
 
1.2%
car 17845
 
1.0%
small_business 17557
 
1.0%
vacation 12441
 
0.7%
moving 12105
 
0.7%
Other values (4) 12299
 
0.7%

Length

2024-07-07T23:33:58.405816image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation 1023751
57.5%
credit_card 402392
 
22.6%
home_improvement 117589
 
6.6%
other 105466
 
5.9%
major_purchase 38262
 
2.1%
medical 21100
 
1.2%
car 17845
 
1.0%
small_business 17557
 
1.0%
vacation 12441
 
0.7%
moving 12105
 
0.7%
Other values (4) 12299
 
0.7%

Most occurring characters

ValueCountFrequency (%)
o 3485038
13.2%
d 2875120
10.9%
t 2685392
10.1%
i 2631554
9.9%
n 2210262
8.3%
e 1977994
7.5%
c 1918185
7.2%
_ 1600651
 
6.0%
a 1585155
 
6.0%
s 1142572
 
4.3%
Other values (12) 4380859
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26492782
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3485038
13.2%
d 2875120
10.9%
t 2685392
10.1%
i 2631554
9.9%
n 2210262
8.3%
e 1977994
7.5%
c 1918185
7.2%
_ 1600651
 
6.0%
a 1585155
 
6.0%
s 1142572
 
4.3%
Other values (12) 4380859
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26492782
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3485038
13.2%
d 2875120
10.9%
t 2685392
10.1%
i 2631554
9.9%
n 2210262
8.3%
e 1977994
7.5%
c 1918185
7.2%
_ 1600651
 
6.0%
a 1585155
 
6.0%
s 1142572
 
4.3%
Other values (12) 4380859
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26492782
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3485038
13.2%
d 2875120
10.9%
t 2685392
10.1%
i 2631554
9.9%
n 2210262
8.3%
e 1977994
7.5%
c 1918185
7.2%
_ 1600651
 
6.0%
a 1585155
 
6.0%
s 1142572
 
4.3%
Other values (12) 4380859
16.5%

revol_bal
Real number (ℝ)

Distinct93490
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16482.243
Minimum0
Maximum2904836
Zeros6779
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:58.448918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1705
Q15969
median11210
Q319963
95-th percentile44347
Maximum2904836
Range2904836
Interquartile range (IQR)13994

Descriptive statistics

Standard deviation22665.016
Coefficient of variation (CV)1.3751172
Kurtosis575.91154
Mean16482.243
Median Absolute Deviation (MAD)6231
Skewness12.568435
Sum2.9351693 × 1010
Variance5.1370297 × 108
MonotonicityNot monotonic
2024-07-07T23:33:58.496100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6779
 
0.4%
8 174
 
< 0.1%
6312 137
 
< 0.1%
5453 132
 
< 0.1%
10 128
 
< 0.1%
2 128
 
< 0.1%
5570 128
 
< 0.1%
5849 127
 
< 0.1%
5232 127
 
< 0.1%
4777 127
 
< 0.1%
Other values (93480) 1772820
99.6%
ValueCountFrequency (%)
0 6779
0.4%
1 94
 
< 0.1%
2 128
 
< 0.1%
3 119
 
< 0.1%
4 113
 
< 0.1%
5 107
 
< 0.1%
6 117
 
< 0.1%
7 97
 
< 0.1%
8 174
 
< 0.1%
9 116
 
< 0.1%
ValueCountFrequency (%)
2904836 1
< 0.1%
2568995 1
< 0.1%
2560703 1
< 0.1%
1746716 1
< 0.1%
1743266 1
< 0.1%
1696796 1
< 0.1%
1470945 1
< 0.1%
1392002 1
< 0.1%
1298783 1
< 0.1%
1190046 1
< 0.1%

revol_util
Real number (ℝ)

Distinct1305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50422159
Minimum0
Maximum3.666
Zeros8251
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size27.2 MiB
2024-07-07T23:33:58.543430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.096
Q10.317
median0.504
Q30.693
95-th percentile0.909
Maximum3.666
Range3.666
Interquartile range (IQR)0.376

Descriptive statistics

Standard deviation0.24612087
Coefficient of variation (CV)0.48812045
Kurtosis-0.80754693
Mean0.50422159
Median Absolute Deviation (MAD)0.188
Skewness-0.0079713773
Sum897921.34
Variance0.060575483
MonotonicityNot monotonic
2024-07-07T23:33:58.591794image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8251
 
0.5%
0.57 3465
 
0.2%
0.48 3459
 
0.2%
0.59 3438
 
0.2%
0.53 3410
 
0.2%
0.58 3409
 
0.2%
0.61 3388
 
0.2%
0.55 3359
 
0.2%
0.54 3341
 
0.2%
0.6 3325
 
0.2%
Other values (1295) 1741962
97.8%
ValueCountFrequency (%)
0 8251
0.5%
0.001 1374
 
0.1%
0.002 1126
 
0.1%
0.003 1036
 
0.1%
0.004 896
 
0.1%
0.005 857
 
< 0.1%
0.006 793
 
< 0.1%
0.007 765
 
< 0.1%
0.008 746
 
< 0.1%
0.009 733
 
< 0.1%
ValueCountFrequency (%)
3.666 1
< 0.1%
1.93 1
< 0.1%
1.846 1
< 0.1%
1.828 1
< 0.1%
1.803 1
< 0.1%
1.777 1
< 0.1%
1.72 1
< 0.1%
1.669 1
< 0.1%
1.658 1
< 0.1%
1.62 1
< 0.1%

sub_grade
Categorical

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
C1
 
114130
B5
 
111327
B4
 
110770
B3
 
103688
C2
 
103646
Other values (30)
1237246 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3561614
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowB2
3rd rowB2
4th rowA3
5th rowB4

Common Values

ValueCountFrequency (%)
C1 114130
 
6.4%
B5 111327
 
6.3%
B4 110770
 
6.2%
B3 103688
 
5.8%
C2 103646
 
5.8%
C3 100634
 
5.7%
C4 100110
 
5.6%
B1 98344
 
5.5%
B2 98055
 
5.5%
C5 92270
 
5.2%
Other values (25) 747833
42.0%

Length

2024-07-07T23:33:58.635949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c1 114130
 
6.4%
b5 111327
 
6.3%
b4 110770
 
6.2%
b3 103688
 
5.8%
c2 103646
 
5.8%
c3 100634
 
5.7%
c4 100110
 
5.6%
b1 98344
 
5.5%
b2 98055
 
5.5%
c5 92270
 
5.2%
Other values (25) 747833
42.0%

Most occurring characters

ValueCountFrequency (%)
B 522184
14.7%
C 510790
14.3%
1 386678
10.9%
4 353356
9.9%
2 350333
9.8%
5 350113
9.8%
3 340327
9.6%
A 328659
9.2%
D 263837
7.4%
E 110477
 
3.1%
Other values (2) 44860
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 522184
14.7%
C 510790
14.3%
1 386678
10.9%
4 353356
9.9%
2 350333
9.8%
5 350113
9.8%
3 340327
9.6%
A 328659
9.2%
D 263837
7.4%
E 110477
 
3.1%
Other values (2) 44860
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 522184
14.7%
C 510790
14.3%
1 386678
10.9%
4 353356
9.9%
2 350333
9.8%
5 350113
9.8%
3 340327
9.6%
A 328659
9.2%
D 263837
7.4%
E 110477
 
3.1%
Other values (2) 44860
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 522184
14.7%
C 510790
14.3%
1 386678
10.9%
4 353356
9.9%
2 350333
9.8%
5 350113
9.8%
3 340327
9.6%
A 328659
9.2%
D 263837
7.4%
E 110477
 
3.1%
Other values (2) 44860
 
1.3%

term
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
36
1330275 
60
450532 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters3561614
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row36
3rd row36
4th row36
5th row36

Common Values

ValueCountFrequency (%)
36 1330275
74.7%
60 450532
 
25.3%

Length

2024-07-07T23:33:58.673407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:58.706253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
36 1330275
74.7%
60 450532
 
25.3%

Most occurring characters

ValueCountFrequency (%)
6 1780807
50.0%
3 1330275
37.4%
0 450532
 
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 1780807
50.0%
3 1330275
37.4%
0 450532
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 1780807
50.0%
3 1330275
37.4%
0 450532
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3561614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 1780807
50.0%
3 1330275
37.4%
0 450532
 
12.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size15.3 MiB
Source Verified
711992 
Not Verified
558561 
Verified
510254 

Length

Max length15
Median length12
Mean length12.053324
Min length8

Characters and Unicode

Total characters21464644
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNot Verified
2nd rowSource Verified
3rd rowVerified
4th rowNot Verified
5th rowNot Verified

Common Values

ValueCountFrequency (%)
Source Verified 711992
40.0%
Not Verified 558561
31.4%
Verified 510254
28.7%

Length

2024-07-07T23:33:58.746287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-07T23:33:58.784647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
verified 1780807
58.4%
source 711992
 
23.3%
not 558561
 
18.3%

Most occurring characters

ValueCountFrequency (%)
e 4273606
19.9%
i 3561614
16.6%
r 2492799
11.6%
V 1780807
8.3%
f 1780807
8.3%
d 1780807
8.3%
o 1270553
 
5.9%
1270553
 
5.9%
S 711992
 
3.3%
u 711992
 
3.3%
Other values (3) 1829114
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21464644
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 4273606
19.9%
i 3561614
16.6%
r 2492799
11.6%
V 1780807
8.3%
f 1780807
8.3%
d 1780807
8.3%
o 1270553
 
5.9%
1270553
 
5.9%
S 711992
 
3.3%
u 711992
 
3.3%
Other values (3) 1829114
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21464644
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 4273606
19.9%
i 3561614
16.6%
r 2492799
11.6%
V 1780807
8.3%
f 1780807
8.3%
d 1780807
8.3%
o 1270553
 
5.9%
1270553
 
5.9%
S 711992
 
3.3%
u 711992
 
3.3%
Other values (3) 1829114
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21464644
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 4273606
19.9%
i 3561614
16.6%
r 2492799
11.6%
V 1780807
8.3%
f 1780807
8.3%
d 1780807
8.3%
o 1270553
 
5.9%
1270553
 
5.9%
S 711992
 
3.3%
u 711992
 
3.3%
Other values (3) 1829114
8.5%

Interactions

2024-07-07T23:33:49.161323image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.064625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.540139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.018068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.618368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.977091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.493258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.926202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.622883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.205408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.591739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.294459image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.909378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.475343image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.048851image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.601046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.263260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.195808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.632199image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.115818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.699038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.070843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.580045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.026735image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.719743image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.288973image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.699869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.394966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.005104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.572362image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.154753image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.694574image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.363271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.311736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.722338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.206756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.779448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.158038image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.665557image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.126070image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.826616image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.373330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.796787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.495575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.100914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.668649image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.247925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.933279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.467232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.399016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.814229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.370941image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.859047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.245305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.750346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.224285image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.924412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.467048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.893033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.595947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.196363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.765699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.341049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.020203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.567241image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.481385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.903901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.467561image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.940386image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.334102image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.834176image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.322628image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.020942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.550510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.986627image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.695093image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.290253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.861521image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.435582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.107210image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.671958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.565189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.999275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.566549image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.032493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.509086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.921879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.422967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.121923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.637299image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.088892image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.799202image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.390500image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.963477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.536487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.196998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.770738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.698327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.095141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.660395image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.122632image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.596631image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.018037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.514690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.219388image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.717819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.184100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.898773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.485369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.065268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.637957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.278531image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.874809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.778432image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.188742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.757883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.206217image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.687937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.112672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.615458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.321673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.803316image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.282717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.011123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.584875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.164112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.738305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.368975image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.974260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.862358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.282455image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.852097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.290798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.779415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.200946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.718728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.418879image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.891475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.385686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.112538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.683118image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.259880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.838534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.458250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.077155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:26.945608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.375641image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:29.949040image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.372947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.869577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.295871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.824123image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.518771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.980475image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.481048image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.215181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.782900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.361854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:46.940379image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.544164image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.179437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.032045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.471312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.052727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.459618image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:32.960100image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.392341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:35.926365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.621825image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.068669image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.582057image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.309658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.891017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.467739image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.043327image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.633585image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.275912image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.112318image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.559862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.152195image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.539882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.047519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.480914image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.022051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.716740image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.150672image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.677347image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.407281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:43.985137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.565992image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.136042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.715942image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.371791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.193544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.647716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.246081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.618960image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.134476image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.567554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.116814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.812448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.231498image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.772273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.506784image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.079365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.657637image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.227591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.798186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.471109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.273180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.735291image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.338460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.697152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.218668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.652298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.210907image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:37.914144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.312545image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.867377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.604582image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.172200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.749725image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.317017image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.882770image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.573891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.358495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.828129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.434427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.788724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.309008image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.740847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.422337image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.012577image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.399724image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:40.965919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.706800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.275374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.853223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.414027image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:48.972034image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:50.667643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:27.443835image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:28.922895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:30.533547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:31.887678image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:33.406883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:34.832674image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:36.525130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:38.119222image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:39.487364image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:41.067721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:42.812056image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:44.377458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:45.954981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:47.511732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-07-07T23:33:49.059394image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-07-07T23:33:50.769579image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-07T23:33:52.179234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

addr_stateannual_incapplication_typeavg_cur_balbc_utilchargeoff_within_12_mthsdelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dlast_fico_range_highlast_fico_range_lowloan_amntloan_statuspurposerevol_balrevol_utilsub_gradetermverification_status
42536NC60000.0Individual476.015.90.00.04.624PROJECT_MANAGER724.0720.0BRENT1.0392.810.10992013-12-01569.0565.012000.0Fully Paiddebt_consolidation7137.00.240B236Not Verified
42537TX39600.0Individual1379.016.10.00.02.492SURGICAL_TECHNICIAN759.0755.0BMORTGAGE2.0157.130.10992013-12-01534.0530.04800.0Fully Paidhome_improvement4136.00.161B236Source Verified
42538MI55000.0Individual9570.053.90.00.022.8710TEAM_LEADERN_CUSTOMER_OPS_&_SYSTEMS734.0730.0BOWN0.0885.460.10992013-12-01834.0830.027050.0Fully Paiddebt_consolidation36638.00.612B236Verified
42539TX96500.0Individual11783.083.50.00.012.613SYSTEMS_ENGINEER709.0705.0AMORTGAGE0.0373.940.07622013-12-01809.0805.012000.0Fully Paiddebt_consolidation13248.00.557A336Not Verified
42540NC88000.0Individual2945.087.70.01.010.024ASSISTANT_DIRECTOR_-_HUMAN_RESOURCES674.0670.0BRENT0.0470.710.12852013-12-01569.0565.014000.0Fully Paiddebt_consolidation3686.00.819B436Not Verified
42541CT105000.0Individual26765.025.00.00.014.0510MANAGER_INFORMATION_DELIVERY764.0760.0AMORTGAGE1.0368.450.06622013-12-01779.0775.012000.0Fully Paiddebt_consolidation13168.00.216A236Not Verified
42542FL63000.0Individual38927.079.10.00.016.512AIRCRAFT_MAINTENANCE_ENGINEER674.0670.0AMORTGAGE0.0476.300.08902013-12-01749.0745.015000.0Fully Paiddebt_consolidation11431.00.742A536Not Verified
42543CA28000.0Individual1440.096.00.00.08.403SPECIAL_ORDER_FULFILLMENT_CLERK664.0660.0CRENT0.0266.340.16242013-12-01634.0630.07550.0Fully Paiddebt_consolidation5759.00.720C536Not Verified
42544CA325000.0Individual53306.067.10.00.018.555AREA_SALES_MANAGER749.0745.0AMORTGAGE1.0872.520.07622013-12-01789.0785.028000.0Fully Paiddebt_consolidation29581.00.546A336Source Verified
42545CO130000.0Individual36362.093.00.00.013.0310LTC719.0715.0BMORTGAGE1.0398.520.11992013-12-01714.0710.012000.0Fully Paiddebt_consolidation10805.00.670B336Source Verified
addr_stateannual_incapplication_typeavg_cur_balbc_utilchargeoff_within_12_mthsdelinq_2yrsdtiemp_lengthemp_titlefico_range_highfico_range_lowgradehome_ownershipinq_last_6mthsinstallmentint_rateissue_dlast_fico_range_highlast_fico_range_lowloan_amntloan_statuspurposerevol_balrevol_utilsub_gradetermverification_status
2925483FL32000.0Joint App2331.052.90.00.070.924SALES_MANAGER709.0705.0CMORTGAGE1.0683.940.13992017-04-01639.0635.029400.0Fully Paiddebt_consolidation12264.00.461C360Source Verified
2925484MD180000.0Individual63816.068.00.00.010.415BRANCH_MANAGER679.0675.0EOWN4.01037.380.25492017-04-01744.0740.035000.0Fully Paiddebt_consolidation31233.00.580E460Source Verified
2925485CA64500.0Individual11056.067.30.00.09.6610FOREMAN664.0660.0FMORTGAGE3.0378.650.28692017-04-01644.0640.012000.0Fully Paidhome_improvement16478.00.513F160Not Verified
2925486IL100000.0Individual28408.014.20.00.08.320INDUSTRIAL_ENGINEER689.0685.0CMORTGAGE1.0814.210.13992017-04-01774.0770.035000.0Fully Paiddebt_consolidation3162.00.142C360Source Verified
2925487CA53000.0Individual4113.092.10.00.024.650NaN664.0660.0ERENT0.0538.400.22742017-04-01584.0580.019200.0Non-Performingdebt_consolidation23058.00.769E160Source Verified
2925488CO107000.0Individual5528.026.01.03.011.650SENIOR_ESCROW_OFFICER674.0670.0ERENT1.0690.300.23992017-04-01504.0500.024000.0Non-Performingother9688.00.249E260Source Verified
2925489PA65000.0Individual3724.024.60.01.019.5510RN729.0725.0AMORTGAGE0.0313.320.07992017-04-01769.0765.010000.0Fully Paiddebt_consolidation9751.00.157A536Source Verified
2925490VA37000.0Individual1021.066.10.00.020.568SALES_ASSOCIATE709.0705.0DRENT1.0358.260.16992017-04-01539.0535.010050.0Non-Performingdebt_consolidation14300.00.470D136Not Verified
2925491NY41000.0Individual3275.05.50.01.019.995CONTACT_INPUT674.0670.0BRENT0.0197.690.11442017-04-01764.0760.06000.0Fully Paidcredit_card1356.00.101B436Source Verified
2925492TX105700.0Individual19955.080.60.01.027.264ASSISTANT_MANAGER699.0695.0EMORTGAGE0.0889.180.25492017-04-01579.0575.030000.0Non-Performingdebt_consolidation15252.00.726E460Verified